Application Deadline for this workshop is October 20, 2017
This workshop will be held at the Penn Pavilion (Level 2), Duke University, Durham, NC.
Monte Carlo sampling methods are an important class of computational algorithms for estimation of high dimensional distributions. They are widely used in physical, chemical, mathematical, and statistical problems and are most useful when it is difficult or impossible to use other methods, due to the high dimensionality of the problem.
Due to its wide application across disciplines, the breakthroughs developed in one field can often lead to advances in other fields.
This SAMSI workshop aims to bring together experts from communities of applied mathematics, statistics, and machine learning working on sampling algorithms to exchange ideas and foster collaborations to make further progress in this broad area.
Schedule and Supporting Media
A schedule will be posted as the event approaches in 2017.
Confirmed Speakers currently include:
- Yves Atchade (University of Michigan)
- Tamara Broderick (MIT)
- Larry Carin (Duke University)
- Arnaud Doucet (Oxford University – GBR)
- Andrew Duncan (Sussex University – GBR)
- Andrew Gelman (Columbia University)
- James Johndrow (Stanford University)
- Omar Ghattas (University of Texas – Austin)
- Shiwei Lan (CalTech)
- Qiang Liu (Dartmouth)
- Lizhen Lin (Notre Dame University)
- Lester Mackey (Stanford University)
- Youssef Marzouk (MIT)
- Antonietta Mira (Università della Svizzera Italiana – CHE)
- Paris Perdikaris (MIT)
- Christian Robert (Université Paris-Dauphine – FRA)
- Andrew Stuart (CalTech)
- Eric Vanden-Eijnden (New York University)
- Jonathan Weare (University of Chicago)
- Clayton Webster (Oak Ridge National Lab)
More information will be made available as the event approaches in 2017.
Questions: email firstname.lastname@example.org